## Parsed with column specification:
## cols(
##   date = col_date(format = ""),
##   sum_mts_swe_m3 = col_double()
## )
## Parsed with column specification:
## cols(
##   year = col_integer(),
##   doy_snowmelt = col_double(),
##   max_doy = col_integer(),
##   median_doy = col_integer()
## )

Whole Navajo Nation

Precipitation

6 month SPI

## 
## Call:
## lm(formula = nn_spi$spi ~ nn_spi$date)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.1086 -0.5897  0.0332  0.6268  2.5886 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  0.6392680  0.1344391   4.755 2.67e-06 ***
## nn_spi$date -0.0000576  0.0000114  -5.054 6.29e-07 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.9752 on 454 degrees of freedom
## Multiple R-squared:  0.05326,    Adjusted R-squared:  0.05118 
## F-statistic: 25.54 on 1 and 454 DF,  p-value: 6.287e-07

NN SPI vs PDSI

## Parsed with column specification:
## cols(
##   date = col_date(format = ""),
##   pdsi = col_double()
## )

  • SPI and PDSI have a pretty high correlation

All mountains SWE vs NN SPI

## TableGrob (2 x 2) "arrange": 4 grobs
##   z     cells    name           grob
## 1 1 (1-1,1-1) arrange gtable[layout]
## 2 2 (1-1,2-2) arrange gtable[layout]
## 3 3 (2-2,1-1) arrange gtable[layout]
## 4 4 (2-2,2-2) arrange gtable[layout]
  • April SPI and high elevation swe anomaly have strongest correlation

without 2010

  • taking out 2010 actually just makes it worse

  • May positive swe & positive spi / positive swe: 50%
  • May negative swe & negative spi / negative swe: 66.6666667%
  • May total correct: 60%

  • April positive swe & positive spi / positive swe: 57.143%
  • April negative swe & negative spi / negative swe: 77.7777778%
  • April total correct: 68.75%

  • every time in april there was a drought warning or a drought emergency, it was preceded by a negative anomaly winter swe

  • drought emergency occured in 2018, with drought warnings in 2006 and 2012
##   waterYear       spi   swe_anom   drought
## 1      2006 -1.243734 -0.8939313   warning
## 2      2012 -1.108299 -0.2017344   warning
## 3      2018 -1.775837 -0.9271395 emergency

Watersheds

CHIRPS SPI vs Gridmet SPI

## Parsed with column specification:
## cols(
##   date = col_date(format = ""),
##   precipitation = col_double()
## )
## Parsed with column specification:
## cols(
##   date = col_date(format = ""),
##   precipitation = col_double()
## )
## Parsed with column specification:
## cols(
##   date = col_date(format = ""),
##   precipitation = col_double()
## )
## Parsed with column specification:
## cols(
##   Date = col_date(format = ""),
##   Precipitation = col_double()
## )

April SPI and Chuska Watershed Anomaly Correlations

  • Middle San Juan April SPI has the highest correlation with Chuska winter swe anom, chinle has the lowest
  • Chinle also has the lowest correlations with other watersheds’ SPIs

May SPI and Chuska Watershed Anomaly Correlations

  • the correlations for may spi across all regions is much lower than for april
  • Middle San Juan still had the higest correlation to Chuska, and Chinle has the least

Middle San Juan

SPI

## 
## Call:
## lm(formula = mid_sj_spi$spi ~ mid_sj_spi$date)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -3.10335 -0.58415  0.02294  0.67057  2.42082 
## 
## Coefficients:
##                   Estimate Std. Error t value Pr(>|t|)    
## (Intercept)      5.015e-01  1.359e-01   3.690 0.000251 ***
## mid_sj_spi$date -4.518e-05  1.152e-05  -3.921 0.000102 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.9857 on 454 degrees of freedom
## Multiple R-squared:  0.03276,    Adjusted R-squared:  0.03063 
## F-statistic: 15.38 on 1 and 454 DF,  p-value: 0.0001017

Chuska Monthly Winter SWE Anomaly vs SPI

## TableGrob (2 x 2) "arrange": 4 grobs
##   z     cells    name           grob
## 1 1 (1-1,1-1) arrange gtable[layout]
## 2 2 (1-1,2-2) arrange gtable[layout]
## 3 3 (2-2,1-1) arrange gtable[layout]
## 4 4 (2-2,2-2) arrange gtable[layout]
  • April SPI has the highest correlation (almost significantly!)

April SPI and Chuska swe

  • April positive swe & positive spi / positive swe: 83.333%
  • April negative swe & negative spi / negative swe: 70%
  • April total correct: 75%

  • drought emergency in 2012 and 2018
##   waterYear watershed       spi   swe_anom   drought
## 1      2006 middle_sj -1.479390 -0.9098490   warning
## 2      2012 middle_sj -1.574148 -0.1368591 emergency
## 3      2018 middle_sj -2.028572 -0.9364887 emergency

Chaco

SPI

## 
## Call:
## lm(formula = chaco_spi$spi ~ chaco_spi$date)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.5627 -0.6231  0.0548  0.6595  2.3534 
## 
## Coefficients:
##                  Estimate Std. Error t value Pr(>|t|)    
## (Intercept)     6.260e-01  1.346e-01   4.651 4.35e-06 ***
## chaco_spi$date -5.639e-05  1.141e-05  -4.942 1.09e-06 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.9764 on 454 degrees of freedom
## Multiple R-squared:  0.05105,    Adjusted R-squared:  0.04896 
## F-statistic: 24.42 on 1 and 454 DF,  p-value: 1.089e-06

Chuska Monthly Winter SWE Anomaly vs SPI

## TableGrob (2 x 2) "arrange": 4 grobs
##   z     cells    name           grob
## 1 1 (1-1,1-1) arrange gtable[layout]
## 2 2 (1-1,2-2) arrange gtable[layout]
## 3 3 (2-2,1-1) arrange gtable[layout]
## 4 4 (2-2,2-2) arrange gtable[layout]
  • April SPI has the highest correlation (almost significantly!)

April SPI and Chuska swe

  • April positive swe & positive spi / positive swe: 50%
  • April negative swe & negative spi / negative swe: 70%
  • April total correct: 62.5%

- drought emergencies in 2006 and 2018

##   waterYear watershed       spi    swe_anom   drought
## 1      2006     chaco -1.663954 -0.90984896 emergency
## 2      2011     chaco -1.052243 -0.08834293   warning
## 3      2012     chaco -1.321458 -0.13685911   warning
## 4      2014     chaco -1.105010 -0.67909787   warning
## 5      2018     chaco -2.226117 -0.93648871 emergency

Upper Puerco

Chuska Monthly Winter SWE Anomaly vs SPI

## TableGrob (2 x 2) "arrange": 4 grobs
##   z     cells    name           grob
## 1 1 (1-1,1-1) arrange gtable[layout]
## 2 2 (1-1,2-2) arrange gtable[layout]
## 3 3 (2-2,1-1) arrange gtable[layout]
## 4 4 (2-2,2-2) arrange gtable[layout]

April SPI and Chuska swe

  • April positive swe & positive spi / positive swe: 66.667%
  • April negative swe & negative spi / negative swe: 80%
  • April total correct: 75%

Drought severity in spring

  • there was a drought emergency in 2006 & 2018
##   waterYear    watershed       spi   swe_anom   drought
## 1      2006 upper_puerco -1.627618 -0.9098490 emergency
## 2      2012 upper_puerco -1.464829 -0.1368591   warning
## 3      2013 upper_puerco -1.008209  0.3904031   warning
## 4      2018 upper_puerco -1.545492 -0.9364887 emergency

Chinle

Precipitation

SPI

## 
## Call:
## lm(formula = chinle_spi$spi ~ chinle_spi$date)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1.55771 -0.44516 -0.02485  0.47069  1.39207 
## 
## Coefficients:
##                   Estimate Std. Error t value Pr(>|t|)    
## (Intercept)      2.175e+00  8.549e-02   25.45   <2e-16 ***
## chinle_spi$date -1.961e-04  7.248e-06  -27.05   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.6202 on 454 degrees of freedom
## Multiple R-squared:  0.6171, Adjusted R-squared:  0.6163 
## F-statistic: 731.8 on 1 and 454 DF,  p-value: < 2.2e-16

Chuska Monthly Winter SWE Anomaly vs SPI

## TableGrob (2 x 2) "arrange": 4 grobs
##   z     cells    name           grob
## 1 1 (1-1,1-1) arrange gtable[layout]
## 2 2 (1-1,2-2) arrange gtable[layout]
## 3 3 (2-2,1-1) arrange gtable[layout]
## 4 4 (2-2,2-2) arrange gtable[layout]

April SPI and Chuska swe

  • April positive swe & positive spi / positive swe: 0%
  • April negative swe & negative spi / negative swe: 80%
  • April total correct: 50%

Drought severity in spring

plot_spi_drought_level(chinle_spi, 
                       month = c(4), 
                       region_time = "Navajo Nation March, April, May 6 month") 

plot_spi_drought_level(chinle_spi, 
                       month = all_months, 
                       region_time = "Navajo Nation March, April, May 6 month",
                       years = "all")

  • drought emergency occured in 2006, 2012, 2018
##   waterYear watershed       spi    swe_anom   drought
## 1      2006    chinle -1.600052 -0.90984896 emergency
## 2      2007    chinle -1.314274 -0.85620097   warning
## 3      2009    chinle -1.094717 -0.14640716   warning
## 4      2011    chinle -1.232336 -0.08834293   warning
## 5      2012    chinle -1.600052 -0.13685911 emergency
## 6      2013    chinle -1.172496  0.39040309   warning
## 7      2014    chinle -1.377450 -0.67909787   warning
## 8      2016    chinle -1.019086  0.04659200   warning
## 9      2018    chinle -1.923309 -0.93648871 emergency
##         watershed april_negative april_positive april_total
## 1 Middle San Juan            0.7      0.8333333       0.750
## 2           Chaco            0.7      0.5000000       0.625
## 3    Upper Puerco            0.8      0.6666667       0.750
## 4          Chinle            0.8      0.0000000       0.500

Relationships between CHuska Swe ANom and April watershed SPI distributions

##             Df Sum Sq Mean Sq F value Pr(>F)  
## drought      3   9.16   3.053   2.723 0.0522 .
## Residuals   60  67.28   1.121                 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## let's test variances
# what are the variances
drought_variances <- drought_times_all %>% 
  group_by(drought) %>% 
  summarize(standard_deviation = round(sd(swe_anom),3),
            variance = round(var(swe_anom),3)) %>% 
  arrange(variance)

# LEvene's test to see if variances are signficiantly different
drought_levene <- leveneTest(swe_anom ~ drought, 
                             data = drought_times_all)
## Warning in leveneTest.default(y = y, group = group, ...): group coerced to
## factor.
# the p value < 0.001 so variances are not equal

Tsaile/wheatfields

## TableGrob (2 x 2) "arrange": 4 grobs
##   z     cells    name           grob
## 1 1 (1-1,1-1) arrange gtable[layout]
## 2 2 (1-1,2-2) arrange gtable[layout]
## 3 3 (2-2,1-1) arrange gtable[layout]
## 4 4 (2-2,2-2) arrange gtable[layout]

Day of year of snowmelt and SPI

April

#### May